Paper Citation Record · LEDGER

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2603.11917.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.11917 v4

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T22:32:36.129552Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T17:00:07.051978Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-06-28T17:02:24.004322Z

Reference resolution

57 of 57 outbound references displayed

verified exact0
verified fuzzy0
unresolved57
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 871c7960-7223-44be-8272-2b07d9badfa3 · outbound

This paper cites Segment anything,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Segment anything,

Reference 1

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Observation 429dba50-21e3-42e2-8496-c08b7baa61d5 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation SAM 2: Segment Anything in Images and Videos

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 8eaae0ae-bbad-4dd4-b1fa-0a2fb56006ca · outbound

This paper cites SAM 3: Segment Anything with Concepts.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation SAM 3: Segment Anything with Concepts

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation d1681fba-195a-49d5-bf89-768cf8dca6d6 · outbound

This paper cites Efficient remote sensing image target detection network with shape-location awareness enhance- ments,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Efficient remote sensing image target detection network with shape-location awareness enhance- ments,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 5a7993fa-a020-4187-b0cf-1ac9b42171c2 · outbound

This paper cites Pi- cosam2: Low-latency segmentation in-sensor for edge vision applica- tions,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Pi- cosam2: Low-latency segmentation in-sensor for edge vision applica- tions,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation dc346267-95ea-4e0a-974c-d5c799afef62 · outbound

This paper cites A novel embedded deep learning wearable sensor for fall detection,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A novel embedded deep learning wearable sensor for fall detection,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 5e0a9eae-67cf-4641-8771-db56165b3f6c · outbound

This paper cites Low-power detection and classification for in-sensor predictive main- tenance based on vibration monitoring,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Low-power detection and classification for in-sensor predictive main- tenance based on vibration monitoring,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 81acb796-58df-485e-86b8-0b349f57a998 · outbound

This paper cites Ultra-efficient on-device object detection on ai-integrated smart glasses,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Ultra-efficient on-device object detection on ai-integrated smart glasses,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 261024c3-7b0f-4396-a14b-a59f84520d20 · outbound

This paper cites Fann-on-mcu: An open-source toolkit for energy-efficient neural network inference at the edge of the internet of things,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Fann-on-mcu: An open-source toolkit for energy-efficient neural network inference at the edge of the internet of things,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 1218550e-317f-48e4-9e0f-53793e03ee44 · outbound

This paper cites Survey and comparison of milliwatts micro controllers for tiny machine learning at the edge,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Survey and comparison of milliwatts micro controllers for tiny machine learning at the edge,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 3a8c7221-5b47-416f-802b-c9126ed813fe · outbound

This paper cites Low latency visual inertial odom- etry with on-sensor accelerated optical flow for resource-constrained uavs,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Low latency visual inertial odom- etry with on-sensor accelerated optical flow for resource-constrained uavs,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation eb251672-dbe1-4618-a37e-7e1d59ba6a3c · outbound

This paper cites A real-time intelligent system based on machine-learning methods for improving communication in sign language,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A real-time intelligent system based on machine-learning methods for improving communication in sign language,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation ae53a18d-c58c-41ed-92cd-e3ee84ec029e · outbound

This paper cites Design and implementation of a resnet-lstm-ghost architecture for gas concentration estimation of electronic noses,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Design and implementation of a resnet-lstm-ghost architecture for gas concentration estimation of electronic noses,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation f8188428-dc96-4dcc-be4d-dee577011472 · outbound

This paper cites A mixture-gas edge-computing multisensor device with generative learning framework,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A mixture-gas edge-computing multisensor device with generative learning framework,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation ef8ee4be-1c8e-4aa5-b4d1-80cf53257303 · outbound

This paper cites Aviear: An iot-based low-power solution for acoustic monitoring of avian species,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Aviear: An iot-based low-power solution for acoustic monitoring of avian species,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 1fe5a6e0-2a06-4f53-b9dd-aa5e0972c97e · outbound

This paper cites Sim- ulation, design, and application of intelligent-edge-based soft magnetic tactile sensor with super-resolution,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Sim- ulation, design, and application of intelligent-edge-based soft magnetic tactile sensor with super-resolution,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation b3a18011-c0f1-42aa-bb3b-cc92dd62a70e · outbound

This paper cites Design and implemen- tation of an arm-based ai module for ectopic beat classification using custom and structural pruned lightweight cnn,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Design and implemen- tation of an arm-based ai module for ectopic beat classification using custom and structural pruned lightweight cnn,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation a336d834-7df9-4005-9fa5-264689809092 · outbound

This paper cites Preliminary analysis of the exploitation of qvar sensor for gesture recognition,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Preliminary analysis of the exploitation of qvar sensor for gesture recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 5df8b94c-f692-4a0d-bb3b-43f171afa658 · outbound

This paper cites Sony imx500,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Sony imx500,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation e35562c1-92fc-4d9f-bdee-e7827c62a9c1 · outbound

This paper cites A 1/2.3inch 12.3mpixel with on-chip 4.97tops/w cnn processor back- illuminated stacked cmos image sensor,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A 1/2.3inch 12.3mpixel with on-chip 4.97tops/w cnn processor back- illuminated stacked cmos image sensor,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 81d8674d-ad5d-429a-814c-2bc8ec41debe · outbound

This paper cites Tinytracker: Ultra- fast and ultra-low-power edge vision for in-sensor gaze estimation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Tinytracker: Ultra- fast and ultra-low-power edge vision for in-sensor gaze estimation,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 5406a069-e97e-4f84-9889-153babf10849 · outbound

This paper cites Near-sensor and in-sensor computing,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Near-sensor and in-sensor computing,

Reference 22

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Observation 58324791-3a71-4f90-b6a4-fb17f7b53680 · outbound

This paper cites TinySAM: Pushing the Envelope for Efficient Segment Anything Model.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation TinySAM: Pushing the Envelope for Efficient Segment Anything Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation a19a1bfa-2296-415c-a073-5203642ca4e1 · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 239530f7-d564-4821-9210-e9aaef263fbd · outbound

This paper cites MobileSAMv2: Faster Segment Anything to Everything.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation MobileSAMv2: Faster Segment Anything to Everything

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation b161ed76-9cd2-4e70-a776-9fc9c00cf2e1 · outbound

This paper cites Lite-sam is actually what you need for segment everything,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Lite-sam is actually what you need for segment everything,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 32c74882-adbd-480e-a77e-f740668d27ea · outbound

This paper cites Efficient deep learning: A survey on making deep learning models smaller, faster, and better,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Efficient deep learning: A survey on making deep learning models smaller, faster, and better,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation c262278e-b0c3-4f36-b90a-a9e314599513 · outbound

This paper cites Mobilenetv4: Universal models for the mobile ecosystem,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Mobilenetv4: Universal models for the mobile ecosystem,

Reference 28

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Observation bcf25206-201f-4213-ba90-21af2a4df683 · outbound

This paper cites Eca-net: Efficient channel attention for deep convolutional neural networks,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Eca-net: Efficient channel attention for deep convolutional neural networks,

Reference 29

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Observation 757b6ce0-f81f-49b4-86ad-394e0ecac6f5 · outbound

This paper cites A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T22:32:36.129552Z

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Observation 7b430b2f-c8cd-44b4-a30f-4915cf5e30d4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 31

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Observation 42724c2f-7820-4d4d-9f0d-75e74a6b3485 · outbound

This paper cites Fast segment anything,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Fast segment anything,

Reference 32

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Observation e71f9e02-4c4a-4627-a131-7e5af323f714 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 33

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Observation 5e7ed4de-3af9-4411-8d15-e846d5c47ccc · outbound

This paper cites Expediting large-scale vision transformer for dense prediction without fine-tuning,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Expediting large-scale vision transformer for dense prediction without fine-tuning,

Reference 34

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Observation a71aa81d-29ef-4d08-a571-1a6a6ab7ff40 · outbound

This paper cites SlimSAM: 0.1% Data Makes Segment Anything Slim.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation SlimSAM: 0.1% Data Makes Segment Anything Slim

Reference 35

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Observation 2d0b5ed2-56be-48d4-8e0e-046e163517f0 · outbound

This paper cites Ptq4sam: Post-training quantization for segment anything,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Ptq4sam: Post-training quantization for segment anything,

Reference 36

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Observation 77454bca-ad01-4c42-932a-3ee3d34e02db · outbound

This paper cites Pq-sam: Post-training quantization for segment anything model,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Pq-sam: Post-training quantization for segment anything model,

Reference 37

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Observation bdfb3d53-ce5e-4096-b470-723d0b4380da · outbound

This paper cites Post training 4-bit quantization of convolutional networks for rapid-deployment,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Post training 4-bit quantization of convolutional networks for rapid-deployment,

Reference 38

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This paper cites Up or down? adaptive rounding for post-training quantization,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Up or down? adaptive rounding for post-training quantization,

Reference 39

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Observation 6ca90036-a152-4974-acb6-b03abb8b3c1a · outbound

This paper cites Hiera: A hierarchical vision transformer without the bells-and-whistles,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Hiera: A hierarchical vision transformer without the bells-and-whistles,

Reference 40

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Observation 617d086d-cf1b-4fcc-be89-da129f55a1a6 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Masked autoencoders are scalable vision learners,

Reference 41

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Observation 7306ba4b-f65b-443d-b67a-d62bcdbc2bf0 · outbound

This paper cites Autoppn: Learning to design promptable pyramid networks for efficient segmentation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Autoppn: Learning to design promptable pyramid networks for efficient segmentation,

Reference 42

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Observation f359a5b6-a89e-41d2-9414-c567ec9dc79c · outbound

This paper cites Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation

Reference 43

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Observation 55e7f143-b95b-4b2d-ac33-f328953b59af · outbound

This paper cites Edge AI-enabled chicken health detection based on enhanced FCOS-Lite and knowledge distillation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Edge AI-enabled chicken health detection based on enhanced FCOS-Lite and knowledge distillation,

Reference 44

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Observation 41fed04d-505c-4978-ad99-9f2fe1a8c0f8 · outbound

This paper cites Pedestrian Warning: Intelligent Vision Sensor vs. Edge AI with LTE C-V2X in a Smart City,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Pedestrian Warning: Intelligent Vision Sensor vs. Edge AI with LTE C-V2X in a Smart City,

Reference 45

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Observation 42b096b8-c1ac-4cb6-a9d6-97e328af32b1 · outbound

This paper cites U-Net: Convolutional net- works for biomedical image segmentation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation U-Net: Convolutional net- works for biomedical image segmentation,

Reference 46

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Observation 11057fa2-39e8-4af5-add3-d755bcfddd67 · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Xception: Deep learning with depthwise separable convolu- tions,

Reference 47

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Observation 40764b99-9763-46ed-940b-64a4ac0a3b04 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Distilling the Knowledge in a Neural Network

Reference 48

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Observation 74f92a9a-63e4-4605-8e6e-99a1eacc5ce3 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 49

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This paper cites V-net: Fully convolutional neu- ral networks for volumetric medical image segmentation,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation V-net: Fully convolutional neu- ral networks for volumetric medical image segmentation,

Reference 50

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Observation 200c0354-6089-41a9-9fc3-933ae605adb0 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 51

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Observation 131a405a-4f9e-43a8-a176-63b30ba45752 · outbound

This paper cites Decoupled weight decay regularization,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Decoupled weight decay regularization,

Reference 52

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This paper cites Microsoft COCO: Common Objects in Context.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Microsoft COCO: Common Objects in Context

Reference 53

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Observation ca81d943-5024-4ec6-a82c-73ebc74c3538 · outbound

This paper cites HPTQ: Hardware-Friendly Post Training Quantization.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation HPTQ: Hardware-Friendly Post Training Quantization

Reference 54

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Observation a6957b24-5333-495e-85b9-26cccb4df47f · outbound

This paper cites Eptq: Enhanced post- training quantization via hessian-guided network-wise optimization,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Eptq: Enhanced post- training quantization via hessian-guided network-wise optimization,

Reference 55

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Observation c818c689-7b89-4ee2-a179-aee85044e83a · outbound

This paper cites Data generation for hardware-friendly post-training quantization,.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation Data generation for hardware-friendly post-training quantization,

Reference 56

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Observation 19e7a59f-1396-4116-b23e-28ada2ad86db · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 57

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Pith citing papers

Observation 1e313e83-5e31-40e4-9f9c-ed1cb0311a83 · inbound

Exploiting In-Sensor Computing for Energy-Efficient Earth Observation cites this paper.

Exploiting In-Sensor Computing for Energy-Efficient Earth Observation PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-06-28T17:02:24.005846Z

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